Information Technology


Top Digital Transformation Trends 2020 for Tech Industry

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The new decade of 2020 or the next stage of "digital evolution" welcomes the world with a promise of hyper intuitive cognitive capabilities and emotionally intelligent interfaces that will rebuild businesses in numerous unpredictable ways. As the tech community (for invested implementation) prepares itself for the new age of disruptive changes to arrive at it's matured stage, it becomes wise and necessary to have a look at these digital transformation trends. Conversational Artificial Intelligence- Siri and Google Assistant are always at swords for their accuracy in answers, but still they both lack in understanding the right intent. Applied conversational AI, fixes this disconnects as it understands the relevance and personalization within humans for successful computer interaction. Conversational AI has an automated speech recognition program that understands natural language and forms a response that exhibits a customized dialogue.


Machine Learning in Python: Principal Component Analysis (PCA) for Handling High-Dimensional Data

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Machine Learning in Python: Principal Component Analysis (PCA) for Handling High-Dimensional Data In this video, I will be showing you how to perform principal component analysis (PCA) in Python using the scikit-learn package. PCA represents a powerful learning approach that enables the analysis of high-dimensional data as well as reveal the contribution of descriptors in governing the distribution of data clusters. Particularly, we will be creating PCA scree plot, scores plot and loadings plot. This video is part of the [Python Data Science Project] series. If you're new here, it would mean the world to me if you would consider subscribing to this channel.


AI at the Edge Still Mostly Consumer, not Enterprise, Market

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Data-driven experiences are rich, immersive and immediate. Think pizza delivery by drone, video cameras that can record traffic accidents at an intersection, freight trucks that can identify a potential system failure. These kinds of fast-acting activities need lots of data -- quickly. So they can't sustain latency as data travels to and from the cloud. That to-and-fro takes too long.



Google is using AI to design AI processors much faster than humans can

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To one extent or another artificial intelligence is practically everywhere these days, from games to image upscaling to smartphone "personal assistants." More than ever, researchers are pouring a ton of time, money, and effort into AI designs. At Google, AI algorithms are even being used to design AI chips. This is not a complete design of silicon that Google is dealing with, but a subset of chip design known as placement optimization. This is a time-consuming task for humans.


Frontier Development Lab Is Going Virtual: Now Accepting Applications For Paid Research Opportunities - SpaceWatch.Global

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The Frontier Development Lab (FDL) Europe applies AI technologies to science to push the frontiers of research and develop new tools to help solve some of the biggest challenges that humanity faces. These range from the effects of climate change to predicting space weather, from improving disaster response, to identifying meteorites that could hold the key to the history of our universe. FDL brings researchers from the cutting-edge of AI and data science, and teams them up with their counterparts from the space sector for an intensive eight-week research sprint, based on a range of challenge areas. The results far exceed what any individual could develop in the same time period, or even in years of individual research. A key aspect of our success is the careful formation of small interdisciplinary teams focused on tackling specific challenges.


Complete Machine Learning and Data Science: Zero to Mastery

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Top 14 AI Use Cases: Artificial Intelligence in Smart Cities 7wData

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Thanks to the advent of the latest innovations in Artificial Intelligence (AI) and machine learning (ML), smart cities -- with a specific focus on the utilities sector -- are undergoing unprecedented changes. The Capgemini Research Institute estimated that, together with the energy sector, the utility vertical can save between $237 billion to $813 billion USD from intelligent automation at scale. Utility companies have been experimenting with AI use cases such as predictive maintenance, yield optimization, and demand/load forecasting. In 2019, more than half of energy and utilities organizations have deployed at least one practical implementation of AI technology, reaping its consistent benefits. Even the public seems eager to enjoy the positive innovations brought forward by the AI transformation.


6 Issues Marketers Need to Consider for Successful AI Implementations

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Despite the many unanswered questions that remain about the use of artificial intelligence (AI) in the workplace and in customer-facing and servicing departments, the growth of AI appears unstoppable. Even as early as two years ago, research from the UK-based digital marketing agency Big Rock found after interviewing 100 senior marketers globally, that AI applications, even at that stage had become one of the marketing departments mainstays. The interviews showed -- again at that stage -- that 55% of companies were either currently implementing or actively investigating some form of AI initiative within their marketing practices. Meaning, AI was already shaking things up in the industry. Unsurprisingly, the research read, this inevitable rise of AI technologies in marketing is causing a major shift in the way companies work.


5 More Things Business Leaders Need to Know About Machine Learning

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In a previous blog post, we explored the importance of machine learning (ML) and delved into the five most important things that business leaders need to know about ML. First, recall that supervised learning is concerned with the prediction and classification of data. Now it's time to dive deeper. We saw that accuracy (the percentage of your data that your model predicts/classifies correctly) is not always the best metric to measure the success of your model, such as when your classes are imbalanced (for example, when 99% of emails are spam and 1% non-spam). Another space where metrics such as accuracy may not be enough is when you need your model to be interpretable.